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140.778.01
Advanced Statistical Computing

Location:
East Baltimore
Term:
2nd term
Department:
Biostatistics
Credits:
3 credits
Academic Year:
2022 - 2023
Instruction Method:
TBD
Class Times:
  • M W,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Kasper Hansen
Frequency Schedule:
Every Other Year
Next Offered:
2024 - 2025
Resources:
Prerequisite:

Prior programming experience; at least one year of doctoral-level statistics/biostatistics theory and methods courses; 140.776

Description:

Covers the theory and application of common algorithms used in statistical computing. Topics include root finding, optimization, numerical integration, Monte Carlo, Markov chain Monte Carlo, stochastic optimization and bootstrapping. Some specific algorithms discussed include: Newton-Raphson, EM, Metropolis-Hastings algorithm, Gibbs sampling, simulated annealing, Gaussian quadrature, Romberg integration, etc. Also discusses applications of these algorithms to real research problems.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Describe common deterministic statistical algorithms, such as root finding, numerical integration methods, Newton-Raphson, quasi-Newton methods, EM
  2. Describe common stochastic algorithms used in statistics, such as Monte Carlo methods, Markov Chain Monte Carlo, stochastic optimization, Gibbs sampling, Metropolis-Hastings method
  3. Understand mathematical properties of common statistical algorithms
  4. Implement statistical algorithms using a high-level statistical programming language
Methods of Assessment:

Method of student evaluation based on computing and theoretical assignments

Instructor Consent:

No consent required